“Given that the supposed greenhouse effect of CO2 depends on concentration changes well after pre-1900, at best (heh!) the effects of “BEST’s homogenization” is to offer a tiny bit more credence to the LIA, and interpretations of recent warming as part of the rebound from it.”
AK,
Let’s see if I can help you.
The global temperature series is important ( impacts published science) in the following ways.
1. It can be used is sensitivity studies. take Lewis and curry as an
example. For there study they look at two periods to calculate Delta T
late 1800s and present day.. Adjustments might change deltaT
by a little amount. sensitivity is related to Dt/Df. The uncertainty in
Df swamps the calculation. Dt is not a sensitive parameter.
2. It can be used to test GCMs. Here the lates period matters most.
3. It can be used to calibrate and validate reconstructions.
I only know of one reconstruction that got different results by using
using raw data for a grid. Basically changing temperatures by a couple tenths here or there wont make the MWP disappear or get warmer.
4. Spectral studies. adjustments do nothing.
In short I dont find any paper that would have to be retracted, have its conclusions changed, by fiddling the adjustments one more time.
what did we set out to do?
1. Skeptics complained about the station drop out. we used all the data.
2. skeptics complained about the non standard methods. we used kriging which they suggested.
3. Skeptics complained about combining stations ( nasaRSM method ).
we did what they suggested and what they had actually published
( see christy )
4. They complained about humans applying adjustments in an unfair manner. We built and tested an algorithm
5. They wanted all the data. we gave it
6. They wanted the code. we gave them SVN.
7 they suggested hiring critics. did that
8. they suggested having professional statisticians. did that.
Now of course we get round two of objections.
1. prove the digital records match the paper records.
2. Get the local field perfect
3. Explain GISS again
4. provide all code changes you have ever made.
5. help me with matlab
6. talk to us even though we really are not users of your data.
7. go place new thermometers all over the world to test your approach.
8. look for sawtooth patterns in stations
9. explain why the algorithm does what it does in these 10,000 cases.
